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2D Correlative-Chain Conditional Random Fields for Semantic Annotation of Web Objects

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Abstract

Semantic annotation of Web objects is a key problem for Web information extraction. The Web contains an abundance of useful semi-structured information about real world objects, and the empirical study shows that strong two-dimensional sequence characteristics and correlative characteristics exist for Web information about objects of the same type across different Web sites. Conditional Random Fields (CRFs) are the state-of-the-art approaches taking the sequence characteristics to do better labeling. However, as the appearance of correlative characteristics between Web object elements, previous CRFs have their limitations for semantic annotation of Web objects and cannot deal with the long distance dependencies between Web object elements efficiently. To better incorporate the long distance dependencies, on one hand, this paper describes long distance dependencies by correlative edges, which are built by making good use of structured information and the characteristics of records from external databases; and on the other hand, this paper presents a two-dimensional Correlative-Chain Conditional Random Fields (2DCC-CRFs) to do semantic annotation of Web objects. This approach extends a classic model, two-dimensional Conditional Random Fields (2DCRFs), by adding correlative edges. Experimental results using a large number of real-world data collected from diverse domains show that the proposed approach can significantly improve the semantic annotation accuracy of Web objects.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, 250014, China

    Yan-Hui Ding (Member, CCF), Qing-Zhong Li (Senior Member, CCF), Yong-Quan Dong (Member, CCF) & Zhao-Hui Peng (Member, CCF)

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  1. Yan-Hui Ding
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  2. Qing-Zhong Li
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  3. Yong-Quan Dong
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  4. Zhao-Hui Peng
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Corresponding author

Correspondence to Qing-Zhong Li.

Additional information

Supported by the National Natural Science Foundation of China under Grant No. 90818001 and the Natural Science Foundation of Shandong Province of China under Grant No. Y2007G24.

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Ding, YH., Li, QZ., Dong, YQ. et al. 2D Correlative-Chain Conditional Random Fields for Semantic Annotation of Web Objects. J. Comput. Sci. Technol. 25, 761–770 (2010). https://doi.org/10.1007/s11390-010-9363-8

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  • DOI: https://doi.org/10.1007/s11390-010-9363-8

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